Adaptive Space Control Using Behavior-Predictive ML Hierarchies
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Solution Overview
Problem
Existing adaptive spaces require manual adjustment of settings by users, who may not be aware of optimal configurations, especially for new occupants, leading to inconvenience and inefficiency.
Innovation Solution
A multi-level machine learning system that processes sensory data from cameras, microphones, and physiological sensors to predict occupant behaviors and automatically adjust lighting, climate, and furniture layout using hierarchical machine learning models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual adjustment of adaptive space settings is implemented, then users can control individual systems, but the operation complexity and time consumption increase significantly
Solution Approach 1:
The system automatically adjusts adaptive space settings by detecting occupant behaviors through sensors and machine learning models, eliminating the need for manual user input. The system serves itself by making intelligent decisions about lighting, temperature, and furniture configuration based on detected behaviors such as sleeping, working, or socializing.
Solution Approach 2:
The machine learning models predict occupant behaviors in advance and pre-adjust the adaptive space settings before the occupant actually needs them. This proactive approach prepares the environment proactively, reducing the time occupants spend on manual adjustments.
2Adaptability or versatility
If multiple adaptive features are added to enhance functionality, then the adaptability of the space improves, but the system complexity increases
Solution Approach 1:
The patent combines multiple adaptive features (lighting control, climate control, robotic furniture) into a unified system managed by a single machine learning framework. This integration allows the system to handle diverse functions through a common architecture, reducing operational complexity despite increased adaptability.
Solution Approach 2:
The machine learning system serves as a universal controller that manages multiple different adaptive features through a single intelligent platform. The behavior mapping module translates detected occupant behaviors into appropriate control settings across various systems, providing multi-functionality without proportionally increasing complexity.
3Adaptability or versatility
If new occupants are added to the adaptive space, then the versatility of the space increases, but the difficulty of detecting and measuring optimal settings increases
Solution Approach 1:
The system continuously monitors occupant behaviors through sensors and uses machine learning models to learn from this data. The feedback loop allows the system to adapt to new occupants by observing their behaviors and adjusting settings accordingly, making the system increasingly accurate over time without manual intervention.
Data Source
AI summary
Systems and methods for automatically adjusting the control settings of one or more adaptive systems in an adaptive space are disclosed. For example, the embodiments provide systems and methods for automatically controlling lighting, climate, and furniture layout in an adaptive space. The systems and methods utilize real-time sensed information from the adaptive space (as well as occupants within the adaptive space) to adaptively modify the living space by adjusting, for example, lighting, climate, and furniture layout. Adaptation is enabled using a hierarchy of machine learning models that can synthesize different kind of sensory information and predict human behaviors in the adaptive space. In response to predicted behaviors, the systems and methods can be used to automatically change aspects of the adaptive space so as to satisfy the behavioral needs of the occupants.